Control system for throttle valve actuating device

ABSTRACT

A control system for a throttle valve actuating device is disclosed. The throttle valve actuating device includes a throttle valve of an internal combustion engine and an actuator for actuating the throttle valve. The control system includes a predictor for predicting a future throttle valve opening and controls the throttle actuating device according to the throttle valve opening predicted by the predictor so that the throttle valve opening coincides with a target opening.

RELATED APPLICATIONS

[0001] This application is a divisional of U.S. patent application Ser.No. 10/043,625, filed on Jan. 10, 2002, and entitled “CONTROL SYSTEM FORPLANT”.

BACKGROUND OF THE INVENTION

[0002] The present invention relates to a control system for a throttlevalve actuating device including a throttle valve of an internalcombustion engine and an actuator for actuating the throttle valve.

[0003] One known control system for controlling a throttle valveactuating device including a motor for actuating the throttle valve andsprings for energizing the throttle valve is disclosed in JapanesePatent Laid-open No. Hei 8-261050. In this control system, the throttlevalve actuating device is controlled with a PID (Proportional, Integral,and Differential) control according to a throttle valve opening detectedby a throttle valve opening sensor.

[0004] Since the throttle valve actuating device includes a dead timeelement which delays the throttle valve movement, the PID control by theabove-described conventional control system may easily become unstableif the control gain of the PID control according to the detectedthrottle valve opening is set to a large value. Therefore, it isnecessary to set the control gain to a value which is sufficiently smallso that the control may not become unstable. However, this results in alow response speed.

SUMMARY OF THE INVENTION

[0005] It is therefore an object of the present invention to provide acontrol system for a throttle valve actuating device, which can improvethe response speed by expanding the stability margin of the control.

[0006] To achieve the above object, the present invention provides acontrol system for a throttle valve actuating device (10) including athrottle valve (3) of an internal combustion engine and actuating means(6) for actuating the throttle valve (3). The control system includespredicting means (23) for predicting a future throttle valve opening(PREDTH) and controls the throttle actuating device (10) according tothe throttle valve opening (PREDTH) predicted by the predicting means(23) so that the throttle valve opening (TH) coincides with a targetopening (THR).

[0007] With this configuration, the throttle actuating device iscontrolled according to the future throttle valve opening predicted bythe predicting means. Accordingly, the stability margin of the controlcan be expanded although the throttle valve actuating device includes adead time element. As a result, the control gain can be set to a largervalue to thereby improve the control response speed.

[0008] Preferably, the throttle valve actuating device (10) is modeledto a controlled object model which includes a dead time element, and thepredicting means (23) predicts a throttle valve opening after the elapseof a dead time (d) due to the dead time element, based on the controlledobject model.

[0009] With this configuration, the throttle valve actuating device (10)is modeled to the controlled object model including a dead time element,and the throttle valve opening after the elapse of a dead time ispredicted based on the controlled object model. Accordingly,compensation of the dead time in the throttle valve actuating device canaccurately be performed.

[0010] Preferably, the control system further includes identifying means(22) for identifying at least one model parameter (a1, a2, b1, c1) ofthe controlled object model. The predicting means (23) predicts thethrottle valve opening (PREDTH) using the at least one model parameter(a1, a2, b1, c1) identified by the identifying means (22).

[0011] With this configuration, the throttle valve opening is predictedusing one or more model parameter identified by the identifying means.Accordingly, it is possible to calculate an accurate predicted throttlevalve opening even when the dynamic characteristic of the throttle valveactuating device has changed due to aging or a change in theenvironmental condition.

[0012] Preferably, the control system further includes a sliding modecontroller (21) for controlling the throttle valve actuating device (10)with a sliding mode control according to a throttle valve opening(PREDTH) predicted by the predicting means (23).

[0013] With this configuration, the throttle valve actuating device iscontrolled with the sliding mode control having good robustness.Accordingly, good stability and controllability of the control can bemaintained even when there exists a modeling error (a difference betweenthe characteristics of the actual plant and the characteristics of thecontrolled object model) due to a difference between the actual deadtime of the throttle valve actuating device and the dead time of thecontrolled object model.

[0014] Preferably, the sliding mode controller (21) controls thethrottle valve actuating device (10) using the at least one modelparameter (a1, a2, b1, c1) identified by the identifying means (22).

[0015] With this configuration, the throttle valve actuating device iscontrolled by the sliding mode controller using one or more modelparameter identified by the identifying means. Accordingly, the modelingerror can be made smaller so that the controllability is furtherimproved.

[0016] Preferably, the control input (Us1) from the sliding modecontroller (21) to the throttle valve actuating device (10) includes anadaptive law input (Uadp).

[0017] With this configuration, better controllability is obtained evenin the presence of disturbance and/or the modeling error.

[0018] The above and other objects, features, and advantages of thepresent invention will become apparent from the following descriptionwhen taken in conjunction with the accompanying drawings whichillustrate embodiments of the present invention by way of example.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1 is a schematic view of a throttle valve control systemaccording to a first embodiment of the present invention;

[0020]FIGS. 2A and 2B are diagrams showing frequency characteristics ofthe throttle valve actuating device shown in FIG. 1;

[0021]FIG. 3 is a functional block diagram showing functions realized byan electronic control unit (ECU) shown in FIG. 1;

[0022]FIG. 4 is a diagram showing the relationship between controlcharacteristics of a sliding mode controller and the value of aswitching function setting parameter (VPOLE);

[0023]FIG. 5 is a diagram showing a range for setting control gains (F,G) of the sliding mode controller;

[0024]FIGS. 6A and 6B are diagrams illustrative of a drift of modelparameters;

[0025]FIGS. 7A through 7C are diagrams showing functions for correctingan identifying error;

[0026]FIG. 8 is a diagram illustrating that a default opening deviationof a throttle valve is reflected to a model parameter (c1′);

[0027]FIG. 9 is a flowchart showing a throttle valve opening controlprocess;

[0028]FIG. 10 is a flowchart showing a process of setting statevariables in the process shown in FIG. 9;

[0029]FIG. 11 is a flowchart showing a process of performingcalculations of a model parameter identifier in the process shown inFIG. 9;

[0030]FIG. 12 is a flowchart showing a process of calculating anidentifying error (ide) in the process shown in FIG. 11;

[0031]FIGS. 13A and 13B are diagrams illustrative of a process oflow-pass filtering on the identifying error (ide);

[0032]FIG. 14 is a flowchart showing the dead zone process in theprocess shown in FIG. 12;

[0033]FIG. 15 is a diagram showing a table used in the process shown inFIG. 14;

[0034]FIG. 16 is a flowchart showing a process of stabilizing a modelparameter vector (θ) in the process shown in FIG. 11;

[0035]FIG. 17 is a flowchart showing a limit process of model parameters(a1′, a2′) in the process shown in FIG. 16;

[0036]FIG. 18 is a diagram illustrative of the change in the values ofthe model parameters in the process shown in FIG. 16;

[0037]FIG. 19 is a flowchart showing a limit process of a modelparameter (b1′) in the process shown in FIG. 16;

[0038]FIG. 20 is a flowchart showing a limit process of a modelparameter (c1′) in the process shown in FIG. 16;

[0039]FIG. 21 is a flowchart showing a process of performingcalculations of a state predictor in the process shown in FIG. 9;

[0040]FIG. 22 is a flowchart showing a process of calculating a controlinput (Usl) in the process shown in FIG. 9;

[0041]FIG. 23 is a flowchart showing a process of calculating apredicted switching function value (σpre) in the process shown in FIG.22;

[0042]FIG. 24 is a flowchart showing a process of calculating theswitching function setting parameter (VPOLE) in the process shown inFIG. 23;

[0043]FIGS. 25A through 25C are diagrams showing maps used in theprocess shown in FIG. 24;

[0044]FIG. 26 is a flowchart showing a process of calculating anintegrated value of the predicted switching function value (σpre) in theprocess shown in FIG. 22;

[0045]FIG. 27 is a flowchart showing a process of calculating a reachinglaw input (Urch) in the process shown in FIG. 22;

[0046]FIG. 28 is a flowchart showing a process of calculating anadaptive law input (Uadp) in the process shown in FIG. 22;

[0047]FIG. 29 is a flowchart showing a process of determining thestability of the sliding mode controller in the process shown in FIG. 9;

[0048]FIG. 30 is a flowchart showing a process of calculating a defaultopening deviation (thdefadp) in the process shown in FIG. 9;

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049]FIG. 1 schematically shows a configuration of a throttle valvecontrol system according to a first embodiment of the present invention.An internal combustion engine (hereinafter referred to as “engine”) 1has an intake passage 2 with a throttle valve 3 disposed therein. Thethrottle valve 3 is provided with a return spring 4 as a firstenergizing means for energizing the throttle valve 3 in a closingdirection, and a resilient member 5 as a second energizing means forenergizing the throttle valve 3 in an opening direction. The throttlevalve 3 can be actuated by a motor 6 as an actuating means through gears(not shown). When the actuating force from the motor 6 is not applied tothe throttle valve 3, an opening TH of the throttle valve 3 ismaintained at a default opening THDEF (for example, 5 degrees) where theenergizing force of the return spring 4 and the energizing force of theresilient member 5 are in equilibrium.

[0050] The motor 6 is connected to an electronic control unit(hereinafter referred to as “ECU”) 7. The operation of the motor 6 iscontrolled by the ECU 7. The throttle valve 3 is associated with athrottle valve opening sensor 8 for detecting the throttle valve openingTH. A detected signal from the throttle valve opening sensor 8 issupplied to the ECU 7.

[0051] Further, the ECU 7 is connected to an acceleration sensor 9 fordetecting a depression amount ACC of an accelerator pedal to detect anoutput demanded by the driver of the vehicle on which the engine 1 ismounted. A detected signal from the acceleration sensor 9 is supplied tothe ECU 7.

[0052] The ECU 7 has an input circuit, an A/D converter, a centralprocessing unit (CPU), a memory circuit, and an output circuit. Theinput circuit is supplied with detected signals from the throttle valveopening sensor 8 and the acceleration sensor 9. The A/D converterconverts input signals into digital signals. The CPU carries out variousprocess operations. The memory circuit has a ROM (read only memory) forstoring processes executed by the CPU, and maps and tables that arereferred to in the processes, a RAM for storing results of executingprocesses by the CPU. The output circuit supplies an energizing currentto the motor 6. The ECU 7 determines a target opening THR of thethrottle valve 3 according to the depression amount ACC of theaccelerator pedal, determines a control quantity DUT for the motor 6 inorder to make the detected throttle valve opening TH coincide with thetarget opening THR, and supplies an electric signal according to thecontrol quantity DUT to the motor 6.

[0053] In the present embodiment, a throttle valve actuating device 10that includes the throttle valve 3, the return spring 4, the resilientmember 5, and the motor 6 is a controlled object. An input to be appliedto the controlled object is a duty ratio DUT of the electric signalapplied to the motor 6. An output from the controlled object is thethrottle valve opening TH detected by the throttle valve opening sensor8.

[0054] When frequency response characteristics of the throttle valveactuating device 10 are measured, gain characteristics and phasecharacteristics indicated by the solid lines in FIGS. 2A and 2B areobtained. A model defined by the equation (1) shown below is set as acontrolled object model. Frequency response characteristics of the modelare indicated by the broken-line curves in FIGS. 2A and 2B. It has beenconfirmed that the frequency response characteristics of the model aresimilar to the characteristics of the throttle valve actuating device10.

DTH(k+1)=a 1×DTH(k)+a 2×DTH(k−1)

+b 1×DUT(k−d)+c 1   (1)

[0055] where k is a parameter representing discrete time, and DTH(k) isa throttle valve opening deviation amount defined by the equation (2)shown below. DTH(k+1) is a throttle valve opening deviation amount at adiscrete time (k+1).

DTH(k)=TH(k)−THDEF   (2)

[0056] where TH is a detected throttle valve opening, and THDEF is thedefault opening.

[0057] In the equation (1), a1, a2, b1, and c1 are parametersdetermining the characteristics of the controlled object model, and d isa dead time. The dead time is a delay between the input and output ofthe controlled object model.

[0058] The model defined by the equation (1) is a DARX model (delayedautoregressive model with exogeneous input) of a discrete time system,which is employed for facilitating the application of an adaptivecontrol.

[0059] In the equation (1), the model parameter c1 which is irrelevantto the input and output of the controlled object is employed, inaddition to the model parameters a1 and a2 which are relevant to theoutput deviation amount DTH and the model parameter b1 which is relevantto the input duty ratio DUT. The model parameter c1 is a parameterrepresenting a deviation amount of the default opening THDEF anddisturbance applied to the throttle valve actuating device 10. In otherwords, the default opening deviation amount and the disturbance can beidentified by identifying the model parameter c1 simultaneously with themodel parameters a1, a2, and b1 by a model parameter identifier.

[0060]FIG. 3 is a functional block diagram of the throttle valve controlsystem which is realized by the ECU 7. The throttle valve control systemas configured includes, an adaptive sliding mode controller 21, a modelparameter identifier 22, a state predictor 23 for calculating apredicted throttle valve opening deviation amount (hereinafter referredto as “predicted deviation amount” or PREDTH(k)) where PREDTH(k)(=DTH(k+d)) after the dead time d has elapsed, and a target openingsetting unit 24 for setting a target opening THR for the throttle valve3 according to the accelerator pedal depression amount ACC.

[0061] The adaptive sliding mode controller 21 calculates a duty ratioDUT according to an adaptive sliding mode control in order to make thedetected throttle valve opening TH coincide with the target opening THR,and outputs the calculated duty ratio DUT.

[0062] By using the adaptive sliding mode controller 21, it is possibleto change the response characteristics of the throttle valve opening THto the target opening THR, using a specific parameter (VPOLE). As aresult, it is possible to avoid shocks at the time the throttle valve 3moves from an open position to a fully closed position, i.e., at thetime the throttle valve 3 collides with a stopper for stopping thethrottle valve 3 in the fully closed position. It is also possible tomake the engine response corresponding to the operation of theaccelerator pedal variable. Further, it is also possible to obtain agood stability against errors of the model parameters.

[0063] The model parameter identifier 22 calculates a corrected modelparameter vector θL (θL^(T)=[a1, a2, b1, c1]) and supplies thecalculated corrected model parameter vector θL to the adaptive slidingmode controller 21. More specifically, the model parameter identifier 22calculates a model parameter vector θ based on the throttle valveopening TH and the duty ratio DUT. The model parameter identifier 22then carries out a limit process of the model parameter vector θ tocalculate the corrected model parameter vector θL, and supplies thecorrected model parameter vector θL to the adaptive sliding modecontroller 21. In this manner, the model parameters a1, a2, and b1 whichare optimum for making the throttle valve opening TH follow up thetarget opening THR are obtained, and also the model parameter c1indicative of disturbance and a deviation amount of the default openingTHDEF is obtained.

[0064] By using the model parameter identifier 22 for identifying themodel parameters on a real-time basis, adaptation to changes in engineoperating conditions, compensation for hardware characteristicsvariations, compensatation for power supply voltage fluctuations, andadaptation to aging-dependent changes of hardware characteristics arepossible.

[0065] The state predictor 23 calculates a throttle valve opening TH(predicted value) after the dead time d has elapsed, or morespecifically a predicted deviation amount PREDTH, based on the throttlevalve opening TH and the duty ratio DUT, and supplies the calculateddeviation amount PREDTH to the adaptive sliding mode controller 21. Byusing the predicted deviation amount PREDTH, the robustness of thecontrol system against the dead time of the controlled object isensured, and the controllability in the vicinity of the default openingTHDEF where the dead time is large is improved.

[0066] Next, principles of operation of the adaptive sliding modecontroller 21 will be hereinafter described.

[0067] First, a target value DTHR(k) is defined as a deviation amountbetween the target opening THR(k) and the default opening THDEF by thefollowing equation (3).

DTHR(k)=THR(k)−THDEF   (3)

[0068] If a deviation e(k) between the throttle valve opening deviationamount DTH and the target value DTHR is defined by the followingequation (4), then a switching function value σ(k) of the adaptivesliding mode controller is set by the following equation (5).

e(k)=DTH(k)−DTHR(k)   (4)

σ(k)=e(k)+VPOLE×e(k−1) =(DTH(k)−DTHR(k)) +VPOLE×(DTH(k−1)−DTHR(k−1))  (5)

[0069] where VPOLE is a switching function setting parameter that is setto a value which is greater than −1 and less than 1.

[0070] On a phase plane defined by a vertical axis representing thedeviation e(k) and a horizontal axis representing the precedingdeviation e(k−1), a pair of the deviation e(k) and the precedingdeviation e(k−1) satisfying the equation of “σ(k)=0” represents astraight line. The straight line is generally referred to as a switchingstraight line. A sliding mode control is a control contemplating thebehavior of the deviation e(k) on the switching straight line. Thesliding mode control is carried out so that the switching function valueσ(k) becomes 0, i.e., the pair of the deviation e(k) and the precedingdeviation e(k−1) exists on the switching straight line on the phaseplane, to thereby achieve a robust control against disturbance and themodeling error (the difference between the characteristics of an actualplant and the characteristics of a controlled object model). As aresult, the throttle valve opening deviation amount DTH is controlledwith good robustness to follow up the target value DTHR.

[0071] As shown in FIG. 4, by changing the value of the switchingfunction setting parameter VPOLE in the equation (5), it is possible tochange damping characteristics of the deviation e(k), i.e., thefollow-up characteristics of the throttle valve opening deviation amountDTH to follow the target value DTHR. Specifically, if VPOLE equals −1,then the throttle valve opening deviation amount DTH completely fails tofollow up the target value DTHR. As the absolute value of the switchingfunction setting parameter VPOLE is reduced, the speed at which thethrottle valve opening deviation amount DTH follows up the target valueDTHR increases.

[0072] The throttle valve control system is required to satisfy thefollowing requirements A1 and A2:

[0073] A1) When the throttle valve 3 is shifted to the fully closedposition, collision of the throttle valve 3 with the stopper forstopping the throttle valve 3 in the fully closed position should beavoided; and

[0074] A2) The controllability with respect to the nonlinearvcharacteristics in the vicinity of the default opening THDEF (a changein the resiliency characteristics due to the equilibrium between theenergizing force of the return spring 4 and the energizing force of theresilient member 5, backlash of gears interposed between the motor 6 andthe throttle valve 3, and a dead zone where the throttle valve openingdoes not change even when the duty ratio DUT changes) should beimproved.

[0075] Therefore, it is necessary to lower the speed at which thedeviation e(k) converges, i.e., the converging speed of the deviatione(k), in the vicinity of the fully closed position of the throttlevalve, and to increase the converging speed of the deviation e(k) in thevicinity of the default opening THDEF.

[0076] According to the sliding mode control, the converging speed ofe(k) can easily be changed by changing the switching function settingparameter VPOLE. Therefore in the present embodiment, the switchingfunction setting parameter VPOLE is set according to the throttle valveopening TH and an amount of change DDTHR (=DTHR(k) −DTHR(k−1)) of thetarget value DTHR, to thereby satisfy the requirements A1 and A2.

[0077] As described above, according to the sliding mode control, thedeviation e(k) is converged to 0 at an indicated converging speed androbustly against disturbance and the modeling error by constraining thepair of the deviation e(k) and the preceding deviation e(k−1) on theswitching straight line (the pair of e(k) and e(k−1) will be hereinafterreferred to as “deviation state quantity”). Therefore, in the slidingmode control, it is important how to place the deviation state quantityonto the switching straight line and constrain the deviation statequantity on the switching straight line.

[0078] From the above standpoint, an input DUT(k) (also indicated asUsl(k)) to the controlled object (an output of the controller) isexpressed as the sum of an equivalent control input Ueq(k), a reachinglaw input Urch(k), and an adaptive law input Uadp(k), as indicated bythe following equation (6).

DUT(k)=Usl(k) =Ueq(k)+Urch(k)+Uadp(k)   (6)

[0079] The equivalent control input Ueq(k) is an input for constrainingthe deviation state quantity on the switching straight line. Thereaching law input Urch(k) is an input for placing deviation statequantity onto the switching straight line. The adaptive law inputUadp(k) is an input for placing deviation state quantity onto theswitching straight line while reducing the modeling error and the effectof disturbance. Methods of calculating these inputs Ueq(k), Urch(k), andUadp(k) will be described below.

[0080] Since the equivalent control input Ueq(k) is an input forconstraining the deviation state quantity on the switching straightline, a condition to be satisfied is given by the following equation(7):

σ(k)=σ(k+1)   (7)

[0081] Using the equations (1), (4), and (5), the duty ratio DUT(k)satisfying the equation (7) is determined by the equation (9) shownbelow. The duty ratio DUT(k) calculated with the equation (9) representsthe equivalent control input Ueq(k). The reaching law input Urch(k) andthe adaptive law input Uadp(k) are defined by the respective equations(10) and (11) shown below. $\begin{matrix}{\begin{matrix}{{D\quad U\quad {T(k)}} = \quad {\frac{1}{b1}\left\{ {{\left( {1 - {a1} - {V\quad P\quad O\quad L\quad E}} \right)D\quad T\quad {H\left( {k + d} \right)}} +} \right.}} \\{\quad {{\left( {{V\quad P\quad O\quad L\quad E} - {a2}} \right)D\quad T\quad {H\left( {k + d - 1} \right)}} - {c1} +}} \\{\quad {{D\quad T\quad H\quad {R\left( {k + d + 1} \right)}} + {\left( {{V\quad P\quad O\quad L\quad E} - 1} \right)D\quad T\quad H\quad {R\left( {k + d} \right)}} -}} \\{\quad \left. {V\quad P\quad O\quad L\quad E \times D\quad T\quad H\quad {R\left( {k + d - 1} \right)}} \right\}} \\{= \quad {U\quad e\quad {q(k)}}}\end{matrix}\quad} & (9) \\{{U\quad r\quad c\quad {h(k)}} = {\frac{- F}{b1}{\sigma \left( {k + d} \right)}}} & (10) \\{{U\quad a\quad {{dp}(k)}} = {\frac{- G}{b1}{\sum\limits_{i = 0}^{k + d}{\Delta \quad T\quad {\sigma (i)}}}}} & (11)\end{matrix}$

[0082] where F and G respectively represent a reaching law control gainand an adaptive law control gain, which are set as described below, andΔT represents a control period.

[0083] Calculating the equation (9) requires a throttle valve openingdeviation amount DTH(k+d) after the elapse of the dead time d and acorresponding target value DTHR(k+d+1). Therefore, the predicteddeviation amount PREDTH(k) calculated by the state predictor 23 is usedas the throttle valve opening deviation amount DTH(k+d) after the elapseof the dead time d, and the latest target value DTHR is used as thetarget value DTHR(k+d+1).

[0084] Next, the reaching law control gain F and the adaptive lawcontrol gain G are determined so that the deviation state quantity canstably be placed onto the switching straight line by the reaching lawinput Urch and the adaptive law input Uadp.

[0085] Specifically, a disturbance V(k) is assumed, and a stabilitycondition for keeping the switching function value σ(k) stable againstthe disturbance V(k) are determined to obtain a condition for settingthe gains F and G. As a result, it has been obtained as the stabilitycondition that the combination of the gains F and G satisfies thefollowing equations (12) through (14), in other words, the combinationof the gains F and G should be located in a hatched region shown in FIG.5.

F>0   (12)

G>0   (13)

F<2−(ΔT/2)G   (14)

[0086] As described above, the equivalent control input Ueq(k), thereaching law input Urch(k), and the adaptive law input Uadp(k) arecalculated from the equations (9) through (11), and the duty ratioDUT(k) is calculated as the sum of those inputs.

[0087] The model parameter identifier 22 calculates a model parametervector of the controlled object model, based on the input (DUT(k)) andoutput (TH(k)) of the controlled object, as described above.Specifically, the model parameter identifier 22 calculates a modelparameter vector θ(k) according to a sequential identifying algorithm(generalized sequential method-of-least-squares algorithm) representedby the following equation (15).

θ(k)=θ(k−1)+KP(k)ide(k)   (15)

θ(k)^(T) =[a1′, a2′, b1′, c1′]  (16)

[0088] where a1′, a2′, b1′, c1′ represent model parameters before alimit process described later is carried out, ide(k) represents anidentifying error defined by the equations (17), (18), and (19) shownbelow, where DTHHAT(k) represents an estimated value of the throttlevalve opening deviation amount DTH(k) (hereinafter referred to as“estimated throttle valve opening deviation amount”) which is calculatedusing the latest model parameter vector θ(k−1), and KP(k) represents again coefficient vector defined by the equation (20) shown below. In theequation (20), P(k) represents a quartic square matrix calculated fromthe equation (21) shown below.

i d e (k)=DTH(k)−DTHHAT(k)   (17)

DTHHAT(k)=θ(k−1)^(T)ζ(k)   (18)

ζ(k)^(T) =[DTH(k−1), DTH(k−2), DUT(k−d−1), 1]  (19)

[0089] $\begin{matrix}{{K\quad {P(k)}} = \frac{{P(k)}{\zeta (k)}}{1 + {{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} & (20) \\{{P\left( {k + 1} \right)} = {\frac{1}{\lambda_{1}}\left( {- \frac{\lambda_{2}{P(k)}{\zeta (k)}{\zeta^{T}(k)}}{\lambda_{1} + {\lambda_{2}{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} \right){P(k)}}} & (21) \\\left( {:{I\quad d\quad e\quad n\quad t\quad i\quad t\quad y\quad M\quad a\quad t\quad r\quad i\quad x}} \right) & \quad\end{matrix}$

[0090] In accordance with the setting of coefficients λ1 and λ2 in theequation (21), the identifying algorithm from the equations (15) through(21) becomes one of the following four identifying algorithm: λ1 = 1, λ2= 0 Fixed gain algorithm λ1 = 1, λ2 = 1 Method-of-least-squaresalgorithm λ1 = 1, λ2 = λ Degressive gain algorithm (λ is a given valueother than 0, 1) λ1 = λ, λ2 = 1 Weighted Method-of- least- squaresalgorithm (λ is a given value other than 0, 1)

[0091] In the present embodiment, it is required that the followingrequirements B1, B2, and B3 are satisfied:

[0092] B1) Adaptation to quasi-static dynamic characteristics changesand hardware characteristics variations

[0093] “Quasi-static dynamic characteristics changes” mean slow-ratecharacteristics changes such as power supply voltage fluctuations orhardware degradations due to aging.

[0094] B2) Adaptation to high-rate dynamic characteristics changes

[0095] Specifically, this means adaptation to dynamic characteristicschanges depending on changes in the throttle valve opening TH.

[0096] B3) Prevention of a drift of model parameters

[0097] The drift, which is an excessive increase of the absolute valuesof model parameters, should be prevented. The drift of model parametersis caused by the effect of the identifying error, which should not bereflected to the model parameters, due to nonlinear characteristics ofthe controlled object.

[0098] In order to satisfy the requirements B1 and B2, the coefficientsλ1 and λ2 are set respectively to a given value λ and “0” so that theweighted Method-of-least-squares algorithm is employed.

[0099] Next, the drift of model parameters will be described below. Asshown in FIG. 6A and FIG. 6B, if a residual identifying error, which iscaused by nonlinear characteristics such as friction characteristics ofthe throttle valve, exists after the model parameters have beenconverged to a certain extent, or if a disturbance whose average valueis not zero is steadily applied, then residual identifying errors areaccumulated, causing a drift of model parameters.

[0100] Since such a residual identifying error should not be reflectedto the values of model parameters, a dead zone process is carried outusing a dead zone function Fnl as shown in FIG. 7A. Specifically, acorrected identifying error idenl(k) is calculated from the followingequation (23), and a model parameter vector θ(k) is calculated using thecorrected identifying error idenl(k). That is, the following equation(15a) is used instead of the above equation (15). In this manner, therequirement B3) can be satisfied.

idenl(k)=Fnl(ide(k))   (23)

θ(k)=θ(k−1)+KP(k)idenl(k)   (15a)

[0101] The dead zone function Fnl is not limited to the function shownin FIG. 7A. A discontinuous dead zone function as shown in FIG. 7B or anincomplete dead zone function as shown in FIG. 7C may be used as thedead zone function Fnl. However, it is impossible to completely preventthe drift if the incomplete dead zone function is used.

[0102] The amplitude of the residual identifying error changes accordingto an amount of change in the throttle valve opening TH. In the presentembodiment, a dead zone width parameter EIDNRLMT which defines the widthof the dead zone shown in FIGS. 7A through 7C is set according to thesquare average value DDTHRSQA of an amount of change in the targetthrottle valve opening THR. Specifically, the dead zone width parameterEIDNRLMT is set such that it increases as the square average valueDDTHRSQA increases. According to such setting of the dead zone widthparameter EIDNRLMT, it is prevented to neglect an identifying error tobe reflected to the values of the model parameters as the residualidentifying error. In the following equation (24), DDTHR represents anamount of change in the target throttle valve opening THR, which iscalculated from the following equation (25): $\begin{matrix}{{D\quad D\quad T\quad H\quad R\quad S\quad Q\quad {A(k)}} = {\frac{1}{n + 1}{\sum\limits_{i = 0}^{n}{D\quad D\quad T\quad H\quad {R(i)}^{2}}}}} & (24) \\{\begin{matrix}{{D\quad D\quad T\quad H\quad {R(k)}} = {{D\quad T\quad H\quad {R(k)}} - {D\quad T\quad H\quad {R\left( {k - 1} \right)}}}} \\{= {{T\quad H\quad {R(k)}} - {T\quad H\quad {R\left( {k - 1} \right)}}}}\end{matrix}\quad} & (25)\end{matrix}$

[0103] Since the throttle valve opening deviation amount DTH iscontrolled to the target value DTHR by the adaptive sliding modecontroller 21, the target value DTHR in the equation (25) may be changedto the throttle valve opening deviation amount DTH. In this case, anamount of change DDTH in the throttle valve opening deviation amount DTHmay be calculated, and the dead zone width parameter EIDNRLMT may be setaccording to the square average value DDTHRSQA obtained by replacingDDTHR in the equation (24) with DDTH.

[0104] For further improving the robustness of the control system, it iseffective to further stabilize the adaptive sliding mode controller 21.In the present embodiment, the elements a1′, a2′, b1′, and c1′of themodel parameter vector 0(k) calculated from the equation (15) aresubjected to the limit process so that a corrected model parametervector θL(k) (θL(k)^(T)=[a1, a2, b1, c1]) is calculated. The adaptivesliding mode controller 21 performs a sliding mode control using thecorrected model parameter vector θL(k). The limit process will bedescribed later in detail referring to the flowcharts.

[0105] Next, a method for calculating the predicted deviation amountPREDTH in the state predictor 23 will be described below.

[0106] First, matrixes A, B and vectors X(k), U(k) are defined accordingto the following equations (26) through (29). $\begin{matrix}{A = \begin{bmatrix}{a1} & {a2} \\1 & 0\end{bmatrix}} & (26) \\{B = \begin{bmatrix}{b1} & {c1} \\0 & 0\end{bmatrix}} & (27) \\{{X(k)} = \begin{bmatrix}{D\quad T\quad {H(k)}} \\{D\quad T\quad {H\left( {k - 1} \right)}}\end{bmatrix}} & (28) \\{{U(k)} = \begin{bmatrix}{D\quad U\quad {T(k)}} \\1\end{bmatrix}} & (29)\end{matrix}$

[0107] By rewriting the equation (1) which defines the controlled objectmodel, using the matrixes A, B and the vectors X(k), U(k), the followingequation (30) is obtained.

X(k+1)=AX(k)+BU(k−d)   (30)

[0108] Determining X(k+d) from the equation (30), the following equation(31) is obtained. $\begin{matrix}{{X\left( {k + d} \right)} = {{A^{d}{X(k)}} + \left\lbrack \begin{matrix}{A^{d - 1}B} & {A^{d - 2}B} & \ldots & {A\quad B} & {\left. B \right\rbrack \quad\begin{bmatrix}{U\left( {k - 1} \right)} \\{U\left( {k - 2} \right)} \\\vdots \\{U\left( {k - d} \right)}\end{bmatrix}}\end{matrix} \right.}} & (31)\end{matrix}$

[0109] If matrixes A′and B′are defined by the following equations (32),(33), using the model parameters a1′, a2′, b1′, and c1′which are notsubjected to the limit process, a predicted vector XHAT(k+d) is given bythe following equation (34). $\begin{matrix}{A^{\prime} = \begin{bmatrix}{a1}^{\prime} & {a2}^{\prime} \\1 & 0\end{bmatrix}} & (32) \\{B^{\prime} = \begin{bmatrix}{b1}^{\prime} & {c1}^{\prime} \\0 & 0\end{bmatrix}} & (33)\end{matrix}$

$\begin{matrix}{{X\quad H\quad A\quad {T\left( {k + d} \right)}} = {{{A^{\prime}}^{d}{X(k)}} + \left\lbrack \begin{matrix}{A^{{\prime \quad d} - 1}B^{\prime}} & {A^{{\prime \quad d} - 2}B^{\prime}} & \ldots & {A^{\prime}\quad B^{\prime}} & {\left. B^{\prime} \right\rbrack \quad\begin{bmatrix}{U\left( {k - 1} \right)} \\{U\left( {k - 2} \right)} \\\vdots \\{U\left( {k - d} \right)}\end{bmatrix}}\end{matrix} \right.}} & (34)\end{matrix}$

[0110] The first-row element DTHHAT(k+d) of the predicted vectorXHAT(k+d) corresponds to the predicted deviation amount PREDTH(k), andis given by the following equation (35).

PREDTH(k)=DTHHAT(k+d) =α1×DTH(k)+α2×DTH(k−1) +β1×DUT( k−1)+β2×DUT(k−2)+.. . +βd×DUT(k−d)+γ1 +γ2 +. . . +γd   (35)

[0111] where α1 represents a first-row, first-column element of thematrix A′^(d), α2 represents a first-row, second-column element of thematrix A′^(d), βi represents a first-row, first-column element of thematrix A′^(d−i)B′, and γi represents a first-row, second-column elementof the matrix A′^(d−i)B′.

[0112] By applying the predicted deviation amount PREDTH(k) calculatedfrom the equation (35) to the equation (9), and replacing the targetvalues DTHR(k+d+1), DTHR(k+d), and DTHR(k+d−1) respectively withDTHR(k), DTHR(k−1), and DTHR(k−2), the following equation (9a) isobtained. From the equation (9a), the equivalent control input Ueq(k) iscalculated. $\begin{matrix}{\begin{matrix}{{D\quad U\quad {T(k)}} = \quad {\frac{1}{b1}\left\{ {{\left( {1 - {a1} - {V\quad P\quad O\quad L\quad E}} \right)P\quad R\quad E\quad D\quad T\quad {H(k)}} +} \right.}} \\{\quad {{\left( {{V\quad P\quad O\quad L\quad E} - {a2}} \right)P\quad R\quad E\quad D\quad T\quad {H\left( {k - 1} \right)}} - {c1} +}} \\{\quad {{D\quad T\quad H\quad {R(k)}} + {\left( {{V\quad P\quad O\quad L\quad E} - 1} \right)D\quad T\quad H\quad {R\left( {k - 1} \right)}} -}} \\{\quad \left. {V\quad P\quad O\quad L\quad E \times D\quad T\quad H\quad {R\left( {k - 2} \right)}} \right\}} \\{= \quad {U\quad e\quad {q(k)}}}\end{matrix}\quad} & \left( \text{9a} \right)\end{matrix}$

[0113] Using the predicted deviation amount PREDTH(k) calculated fromthe equation (35), a predicted switching function value σpre(k) isdefined by the following equation (36). The reaching law input Urch(k)and the adaptive law input Uadp(k) are calculated respectively from thefollowing equations (10a) and (11a).

σpre(k)=(PREDTH(k)−DTHR(k−1)) +VPOLE(PREDTH(k−1)−DTHR(k−2))   (36)

[0114] $\begin{matrix}{{U\quad r\quad c\quad {h(k)}} = {\frac{- F}{b1}\sigma \quad p\quad r\quad {e(k)}}} & \left( \text{10a} \right) \\{{U\quad a\quad d\quad {p(k)}} = {\frac{- G}{b1}{\sum\limits_{i = 0}^{k}{\Delta \quad T\quad \sigma \quad p\quad r\quad {e(i)}}}}} & \left( \text{11a} \right)\end{matrix}$

[0115] The model parameter c1′ is a parameter representing a deviationof the default opening THDEF and disturbance. Therefore, as shown inFIG. 8, the model parameter c1′ changes with disturbance, but can beregarded as substantially constant in a relatively short period. In thepresent embodiment, the model parameter c1′ is statistically processed,and the central value of its variations is calculated as a defaultopening deviation thdefadp. The default opening deviation thdefadp isused for calculating the throttle valve opening deviation amount DTH andthe target value DTHR.

[0116] Generally, the method of least squares is known as a method ofthe statistic process. In the statistic process according to the methodof least squares, all data, i.e., all identified parameters c1′,obtained in a certain period are stored in a memory and the stored datais subjected to a batch calculation of the statistic process at acertain timing. However, the batch calculation requires a memory havinga large storage capacity for storing all data, and an increased amountof calculations are necessary because inverse matrix calculations arerequired.

[0117] Therefore, according to the present embodiment, the sequentialmethod-of-least-squares algorithm for adaptive control which isindicated by the equations (15) through (21) is applied to the statisticprocess, and the central value of the least squares of the modelparameter cl is calculated as the default opening deviation thdefadp.

[0118] Specifically, in the equations (15) through (21), by replacingθ(k) and θ(k)^(T) with thdefadp, replacing ζ(k) and ζ(k)^(T) with “1”,replacing ide(k) with ec1(k), replacing KP(k) with KPTH(k), replacingP(k) with PTH(k), and replacing λ1 and λ2 respectively with λ1′ and λ2′,the following equations (37) through (40) are obtained.

thdefadp(k+1)=thdefadp(k)+KPTH(k)ec1(k)   (37)

[0119] $\begin{matrix}{{K\quad P\quad T\quad {H(k)}} = \frac{P\quad T\quad {H(k)}}{1 + {P\quad T\quad {H(k)}}}} & (38) \\{{P\quad T\quad {H\left( {k + 1} \right)}} = {\frac{1}{\lambda_{1}^{\prime}}\left( {1 - \frac{\lambda_{2}^{\prime}P\quad T\quad {H(k)}}{\lambda_{1}^{\prime} + {\lambda_{2}^{\prime}P\quad T\quad {H(k)}}}} \right)P\quad T\quad {H(k)}}} & (39)\end{matrix}$

ec1(k)=c1′(k)−thdefadp(k)   (40)

[0120] One of the four algorithms described above can be selectedaccording to the setting of the coefficients λ1′ and λ2′. In theequation (39), the coefficient λ1′ is set to a given value other than 0or 1, and the coefficient λ2′ is set to 1, thus employing the weightedmethod of least squares.

[0121] For the calculations of the equations (37) through (40), thevalues to be stored are thdefadp(k+1) and PTh(k+1) only, and no inversematrix calculations are required. Therefore, by employing the sequentialmethod-of-least-squares algorithm, the model parameter c1 can bestatistically processed according to the method of least squares whileovercoming the shortcomings of a general method of least squares.

[0122] The default opening deviation thdefadp obtained as a result ofthe statistic process is applied to the equations (2) and (3), and thethrottle valve opening deviation amount DTH(k) and the target valueDTHR(k) are calculated from the following equations (41) and (42)instead of the equations (2) and (3).

DTH(k)=TH(k)−THDEF+thdefadp   (41)

DTHR(k)=THR(k)−THDEF+thdefadp   (42)

[0123] Using the equations (41) and (42), even when the default openingTHDEF is shifted from its designed value due to characteristicvariations or aging of the hardware, the shift can be compensated toperform an accurate control process.

[0124] Operation processes executed by the CPU in the ECU 7 forrealizing the functions of the adaptive sliding mode controller 21, themodel parameter identifier 22, and the state predictor 23 will bedescribed below.

[0125]FIG. 9 is a flowchart showing a process of the throttle valveopening control. The process is executed by the CPU in the ECU 7 inevery predetermined period of time (e.g., 2 msec).

[0126] In step S11, a process of setting a state variable shown in FIG.10 is performed. Calculations of the equations (41) and (42) areexecuted to determine the throttle valve opening deviation amount DTH(k)and the target value DTHR(k) (steps S21 and S22 in FIG. 10). The symbol(k) representing a current value may sometimes be omitted as shown inFIG. 10.

[0127] In step S12, a process of performing calculations of the modelparameter identifier as shown in FIG. 11, i.e., a process of calculatingthe model parameter vector θ(k) from the equation (15a), is carried out.Further, the model parameter vector θ(k) is subjected to the limitprocess so that the corrected model parameter vector θL(k) iscalculated.

[0128] In step S13, a process of performing calculations of the statepredictor as shown in FIG. 21 is carried out to calculate the predicteddeviation amount PREDTH(k).

[0129] Next, using the corrected model parameter vector θL(k) calculatedin step S12, a process of calculating the control input Usl(k) as shownin FIG. 22 is carried out in step S14. Specifically, the equivalentcontrol input Ueq, the reaching law input Urch(k), and the adaptive lawinput Uadp(k) are calculated, and the control input Usl(k) (=duty ratioDUT(k)) is calculated as a sum of these inputs Ueq(k), Urch(k), andUadp(k).

[0130] In step S16, a process of stability determination of the slidingmode controller as shown in FIG. 29 is carried out. Specifically, thestability is determined based on a differential value of the Lyapunovfunction, and a stability determination flag FSMCSTAB is set. When thestability determination flag FSMCSTAB is set to “1”, this indicates thatthe adaptive sliding mode controller 21 is unstable.

[0131] If the stability determination flag FSMCSTAB is set to “1”,indicating that the adaptive sliding mode controller 21 is unstable, theswitching function setting parameter VPOLE is set to a predeterminedstabilizing value XPOLESTB (see steps S231 and S232 in FIG. 24), and theequivalent control input Ueq is set to “0”. That is, the control processby the adaptive sliding mode controller 21 is switched to a controlprocess based on only the reaching law input Urch and the adaptive lawinput Uadp, to thereby stabilize the control (see steps S206 and S208 inFIG. 22).

[0132] Further, when the adaptive sliding mode controller 21 has becomeunstable, the equations for calculating the reaching law input Urch andthe adaptive law input Uadp are changed. Specifically, the values of thereaching law control gain F and the adaptive law control gain G arechanged to values for stabilizing the adaptive sliding mode controller21, and the reaching law input Urch and the adaptive law input Uadp arecalculated without using the model parameter b1 (see FIGS. 27 and 28).According to the above stabilizing process, it is possible to quicklyterminate the unstable state of the adaptive sliding mode controller 21,and to bring the adaptive sliding mode controller 21 back to its stablestate.

[0133] In step S17, a process of calculating the default openingdeviation thdefadp as shown in FIG. 30 is performed to calculate thedefault opening deviation thdefadp.

[0134]FIG. 11 is a flowchart showing a process of performingcalculations of the model parameter identifier 22.

[0135] In step S31, the gain coefficient vector KP(k) is calculated fromthe equation (20). Then, the estimated throttle valve opening deviationamount DTHHAT(k) is calculated from the equation (18) in step S32. Instep S33, a process of calculating the identifying error idenl(k) asshown in FIG. 12 is carried out. The estimated throttle valve openingdeviation amount DTHHAT(k) calculated in step S32 is applied to theequation (17) to calculate the identifying error ide(k). Further in stepS32, the dead zone process using the function shown in FIG. 7A iscarried out to calculate the corrected identifying error idenl.

[0136] In step S34, the model parameter vector θ(k) is calculated fromthe equation (15a). Then, the model parameter vector θ(k) is subjectedto the stabilization process in step S35. That is, each of the modelparameters is subjected to the limit process to calculate the correctedmodel parameter vector θL(k).

[0137]FIG. 12 is a flowchart showing a process of calculating theidentifying error idenl(k) which is carried out in step S33 shown inFIG. 11.

[0138] In step S51, the identifying error ide(k) is calculated from theequation (17). Then, it is determined whether or not the value of acounter CNTIDST which is incremented in step S53 is greater than apredetermined value XCNTIDST that is set according to the dead time d ofthe controlled object (step S52). The predetermined value XCNTIDST isset, for example, to “3” according to a dead time d=2. Since the counterCNTIDST has an initial value of “0”, the process first goes to step S53,in which the counter CNTIDST is incremented by “1”. Then, theidentifying error ide(k) is set to “0” in step S54, after which theprocess goes to step S55. Immediately after starting identifying themodel parameter vector θ(k), no correct identifying error can beobtained by the equation (17). Therefore, the identifying error ide(k)is set to “0” according to steps S52 through S54, instead of using thecalculated result of the equation (17).

[0139] If the answer to the step S52 is affirmative (YES), then theprocess immediately proceeds to step S55.

[0140] In step S55, the identifying error ide(k) is subjected to alow-pass filtering. Specifically, when identifying the model parametersof an controlled object which has low-pass characteristics, theidentifying weight of the method-of-least-squares algorithm for theidentifying error ide(k) has frequency characteristics as indicated bythe solid line L1 in FIG. 13A. By the low-pass filtering of theidentifying error ide(k), the frequency characteristics as indicated bythe solid line L1 is changed to a frequency characteristics as indicatedby the broken line L2 where the high-frequency components areattenuated. The reason for executing the low-pass filtering will bedescribed below.

[0141] The frequency characteristics of the actual controlled objecthaving low-pass characteristics and the controlled object model thereofare represented respectively by the solid lines L3 and L4 in FIG. 13B.Specifically, if the model parameters are identified by the modelparameter identifier 22 with respect to the controlled object which haslow-pass characteristics (characteristics of attenuating high-frequencycomponents), the identified model parameters are largely affected by thehigh-frequency-rejection characteristics so that the gain of thecontrolled object model becomes lower than the actual characteristics ina low-frequency range. As a result, the sliding mode controller 21excessively corrects the control input.

[0142] By changing the frequency characteristics of the weighting of theidentifying algorithm to the characteristics indicated by the brokenline L2 in FIG. 13A according to the low-pass filtering, the frequencycharacteristics of the controlled object are changed to frequencycharacteristics indicated by the broken line L5 in FIG. 13B. As aresult, the frequency characteristics of the controlled object model ismade to coincide with the actual frequency characteristics, or the lowfrequency gain of the controlled object model is corrected to a levelwhich is slightly higher than the actual gain. Accordinly, it ispossible to prevent the control input from being excessively correctedby the sliding mode controller 21, to thereby improve the robustness ofthe control system and further stabilize the control system.

[0143] The low-pass filtering is carried out by storing past valueside(k−i) of the identifying error (e.g., 10 past values for i=1 through10) in a ring buffer, multiplying the past values by weightingcoefficients, and adding the products of the past values and theweighting coefficients.

[0144] Since the identifying error ide(k) is calculated from theequations (17), (18), and (19), the same effect as described above canbe obtained by performing the same low-pass filtering on the throttlevalve opening deviation amount DTH(k) and the estimated throttle valveopening deviation amount DTHHAT(k), or by performing the same low-passfiltering on the throttle valve opening deviation amounts DTH(k−1),DTH(k−2) and the duty ratio DUT(k−d−1).

[0145] Referring back to FIG. 12, the dead zone process as shown in FIG.14 is carried out in step S56. In step S61 shown in FIG. 14, “n” in theequation (24) is set, for example, to “5” to calculate the squareaverage value DDTHRSQA of an amount of change of the target throttlevalve opening THR. Then, an EIDNRLMT table shown in FIG. 15 is retrievedaccording to the square average value DDTHRSQA to calculate the deadzone width parameter EIDNRLMT (step S62).

[0146] In step S63, it is determined whether or not the identifyingerror ide(k) is greater than the dead zone width parameter EIDNRLMT. Ifide(k) is greater than EIDNRLMT, the corrected identifying erroridenl(k) is calculated from the following equation (43) in step S67.

idenl(k)=ide(k)−EIDNRLMT   (43)

[0147] If the answer to step S63 is negative (NO), it is determinedwhether or not the identifying error ide(k) is greater than the negativevalue of the dead zone width parameter EIDNRLMT with a minus sign (stepS64).

[0148] If ide(k) is less than −EIDNRLMT, the corrected identifying erroridenl(k) is calculated from the following equation (44) in step S65.

idenl(k)=ide(k)+EIDNRLMT   (44)

[0149] If the identifying error ide(k) is in the range between +EIDNRLMTand −EIDNRLMT, the corrected identifying error idenl(k) is set to “0” instep S66.

[0150]FIG. 16 is a flowchart showing a process of stabilizing the modelparameter vector θ(k), which is carried out in step S35 shown in FIG.11.

[0151] In step S71 shown in FIG. 16, flags FA1STAB, FA2STAB, FB1LMT, andFC1LMT used in this process are initialized to be set to “0”. In stepS72, the limit process of the model parameters a1′ and a2′ shown in FIG.17 is executed. In step S73, the limit process of the model parameterb1′ shown in FIG. 19 is executed. In step S74, the limit process of themodel parameter c1′ shown in FIG. 20 is executed.

[0152]FIG. 17 is a flowchart showing the limit process of the modelparameters a1′ and a2′, which is carried out in the step S72 shown inFIG. 16. FIG. 18 is a diagram illustrative of the process shown in FIG.17, and will be referred to with FIG. 17.

[0153] In FIG. 18, combinations of the model parameters a1′ and a2′which are required to be limited are indicated by “x” symbols, and therange of combinations of the model parameters a1′ and a2′ which arestable are indicated by a hatched region (hereinafter referred to as“stable region”). The limit process shown in FIG. 17 is a process ofmoving the combinations of the model parameters a1′ and a2′ which are inthe outside of the stable region into the stable region at positionsindicated by “◯” symbols.

[0154] In step S81, it is determined whether or not the model parametera2′ is greater than or equal to a predetermined a2 lower limit valueXIDA2L. The predetermined a2 lower limit value XIDA2L is set to anegative value greater than “−1”. Stable corrected model parameters a1and a2 are obtained when setting the predetermined a2 lower limit valueXIDA2L to “−1”. However, the predetermined a2 lower limit value XIDA2Lis set to a negative value greater than “−1” because the matrix Adefined by the equation (26) to the “n”th power may occasionally becomeunstable (which means that the model parameters a1′ and a2′ do notdiverge, but oscillate).

[0155] If a2′ is less than XIDA2L in step S81, the corrected modelparameter a2 is set to the lower limit value XIDA2L, and an a2stabilizing flag FA2STAB is set to “1”. When the a2 stabilizing flagFA2STAB is set to “1”, this indicates that the corrected model parametera2 is set to the lower limit value XIDA2L. In FIG. 18, the correction ofthe model parameter in a limit process P1 of steps S81 and S82 isindicated by the arrow lines with “P1”.

[0156] If the answer to the step S81 is affirmative (YES), i.e., if a2′is greater than or equal to XIDA2L, the corrected model parameter a2 isset to the model parameter a2′ in step S83.

[0157] In steps S84 and S85, it is determined whether or not the modelparameter a1′ is in a range defined by a predetermined al lower limitvalue XIDA1L and a predetermined al upper limit value XIDA1H. Thepredetermined al lower limit value XIDA1L is set to a value which isequal to or greater than “−2” and lower than “0”, and the predetermineda1 upper limit value XIDA1H is set to “2”, for example.

[0158] If the answers to steps S84 and S85 are affirmative (YES), i.e.,if a1′ is greater than or equal to XIDA1L and less than or equal toXIDA1H, the corrected model parameter a1 is set to the model parametera1′ in step S88.

[0159] If a1′ is less than XIDA1L in step S84, the corrected modelparameter a1 is set to the lower limit value XIDA1L and an a1stabilizing flag FA1STAB is set to “1” in step S86. If a1′ is greaterthan XIDA1H in step S85, the corrected model parameter a1 is set to theupper limit value XIDA1H and the al stabilizing flag FA1STAB is set to“1” in step S87. When the a1 stabilizing flag FA1STAB is set to “1”,this indicates that the corrected model parameter a1 is set to the lowerlimit value XIDA1L or the upper limit value XIDA1H. In FIG. 18, thecorrection of the model parameter in a limit process P2 of steps S84through S87 is indicated by the arrow lines with “P2 ”.

[0160] In step S90, it is determined whether or not the sum of theabsolute value of the corrected model parameter al and the correctedmodel parameter a2 is less than or equal to a predetermined stabilitydetermination value XA2STAB. The predetermined stability determinationvalue XA2STAB is set to a value close to “1” but less than “1” (e.g.,“0.99”).

[0161] Straight lines L1 and L2 shown in FIG. 18 satisfy the followingequation (45).

a2+|a1|=XA2STAB   (45)

[0162] Therefore, in step S90, it is determined whether or not thecombination of the corrected model parameters a1 and a2 is placed at aposition on or lower than the straight lines L1 and L2 shown in FIG. 18.If the answer to step S90 is affirmative (YES), the limit processimmediately ends, since the combination of the corrected modelparameters a1 and a2 is in the stable region shown in FIG. 18.

[0163] If the answer to step S90 is negative (NO), it is determinedwhether or not the corrected model parameter a1 is less than or equal toa value obtained by subtracting the predetermined a2 lower limit valueXIDA2L from the predetermined stability determination value XA2STAB instep S91 (since XIDA2L is less than “0”, XA2STAB-XIDA2L is greater thanXA2STAB). If the corrected model parameter a1 is equal to or less than(XA2STAB−XIDA2L), the corrected model parameter a2 is set to(XA2STAB−|a1|) and the a2 stabilizing flag FA2STAB is set to “1” in stepS92.

[0164] If the corrected model parameter a1 is greater than(XA2STAB−XIDA2L) in step S91, the corrected model parameter a1 is set to(XA2STAB−XIDA2L) in step S93. Further in step S93, the corrected modelparameter a2 is set to the predetermined a2 lower limit value XIDA2L,and the al stabilizing flag FA1STAB and the a2 stabilizing flag FA2STABare set to “1” in step S93.

[0165] In FIG. 18, the correction of the model parameter in a limitprocess P3 of steps S91 and S92 is indicated by the arrow lines with“P3”, and the correction of the model parameter in a limit processP4 insteps S91 and S93 is indicated by the arrow lines with “P4”.

[0166] As described above, the limit process shown in FIG. 17 is carriedout to bring the model parameters a1′ and a2′ into the stable regionshown in FIG. 18, thus calculating the corrected model parameters a1 anda2.

[0167]FIG. 19 is a flowchart showing a limit process of the modelparameter b1′, which is carried out in step S73 shown in FIG. 16.

[0168] In steps S101 and S102, it is determined whether or not the modelparameter b1′ is in a range defined by a predetermined b1 lower limitvalue XIDB1L and a predetermined b1 upper limit value XIDB1H. Thepredetermined bl lower limit value XIDB1L is set to a positive value(e.g., “0.1”), and the predetermined b1 upper limit value XIDB1H is setto “1”, for example.

[0169] If the answers to steps S101 and S102 are affirmative (YES),i.e., if b1′ is greater than or equal to XIDB1L and less than or equalto XIDB1H, the corrected model parameter b1 is set to the modelparameter b1′ in step S105.

[0170] If b1′ is less than XIDB1L in step S101, the corrected modelparameter b1 is set to the lower limit value XIDB1L, and a b1 limitingflag FB1LMT is set to “1” in step S104. If b1′ is greater than XIDB1H instep S102, then the corrected model parameter b1 is set to the upperlimit value XIDB1H, and the bl limiting flag FB1LMT is set to “1” instep S103. When the b1 limiting flag FB1LMT is set to “1”, thisindicates that the corrected model parameter b1 is set to the lowerlimit value XIDB1L or the upper limit value XIDB1H.

[0171]FIG. 20 is a flowchart showing a limit process of the modelparameter c1′, which is carried out in step S74 shown in FIG. 16.

[0172] In steps S111 and S112, it is determined whether or not the modelparameters c1′ is in a range defined by a predetermined c1 lower limitvalue XIDC1L and a predetermined c1 upper limit value XIDC1H. Thepredetermined c1 lower limit value XIDC1L is set to “−60”, for example,and the predetermined c1 upper limit value XIDC1H is set to “60”, forexample.

[0173] If the answers to steps S111 and S112 are affirmative (YES),i.e., if c1′ is greater than or equal to XIDC1L and less than or equalto XIDC1H, the corrected model parameter c1 is set to the modelparameter c1′ in step S115.

[0174] If c1′ is less than XIDC1L in step S111, the corrected modelparameter c1 is set to the lower limit value XIDC1L, and a c1 limitingflag FC1LMT is set to “1” in step S114. If c1′ is greater than XIDC1H instep S112, the corrected model parameter c1 is set to the upper limitvalue XIDC1H, and the c1 limiting flag FC1LMT is set to “1” in stepS113. When the c1 limiting flag FC1LMT is set to “1”, this indicatesthat the corrected model parameter c1 is set to the lower limit valueXIDC1L or the upper limit value XIDC1H.

[0175]FIG. 21 is a flowchart showing a process of calculations of thestate predictor, which is carried out in step S13 shown in FIG. 9.

[0176] In step S121, the matrix calculations are executed to calculatethe matrix elements α1, α2, β1, β2, γ1 through γd in the equation (35).

[0177] In step S122, the predicted deviation amount PREDTH(k) iscalculated from the equation (35).

[0178]FIG. 22 is a flowchart showing a process of calculation of thecontrol input Usl (=DUT) applied to the throttle valve actuating device10, which is carried out in step S14 shown in FIG. 9.

[0179] In step S201, a process of calculation of the predicted switchingfunction value σpre, which is shown in FIG. 23, is executed. In stepS202, a process of calculation of the integrated value of the predictedswitching function value σpre, which is shown in FIG. 26, is executed.In step S203, the equivalent control input Ueq is calculated from theequation (9). In step S204, a process of calculation of the reaching lawinput Urch, which is shown in FIG. 27, is executed. In step S205, aprocess of calculation of the adaptive law input Uadp, which is shown inFIG. 28, is executed.

[0180] In step S206, it is determined whether or not the stabilitydetermination flag FSMCSTAB set in the process shown in FIG. 29 is “1”.When the stability determination flag FSMCSTAB is set to “1”, thisindicates that the adaptive sliding mode controller 21 is unstable.

[0181] If FSMCSTAB is “0” in step S206, indicating that the adaptivesliding mode controller 21 is stable, the control inputs Ueq, Urch, andUadp which are calculated in steps S203 through S205 are added, therebycalculating the control input Usl in step S207.

[0182] If FSMCSTAB is “1” in step S206, indicating that the adaptivesliding mode controller 21 is unstable, the sum of the reaching lawinput Urch and the adaptive law input Uadp is calculated as the controlinput Usl. In other words, the equivalent control input Ueq is not usedfor calculating the control input Usl, thus preventing the controlsystem from becoming unstable.

[0183] In steps S209 and S210, it is determined whether or not thecalculated control input Usl is in a range defined between apredetermined upper limit value XUSLH and a predetermined lower limitvalue XUSLL. If the control input Usl is in the range between XUSLH andXUSLL, the process immediately ends. If the control input Usl is lessthan or equal to the predetermined lower limit value XUSLL in step S209,the control input Usl is set to the predetermined lower limit valueXUSLL in step S212. If the control input Usl is greater than or equal tothe predetermined upper limit value XUSLH in step S210, the controlinput Usl is set to the predetermined upper limit value XUSLH in stepS211.

[0184]FIG. 23 is a flowchart showing a process of calculating thepredicted switching function value σpre, which is carried out in stepS201 shown in FIG. 22.

[0185] In step S221, the process of calculating the switching functionsetting parameter VPOLE, which is shown in FIG. 24 is executed. Then,the predicted switching function value σpre(k) is calculated from theequation (36) in step S222.

[0186] In steps S223 and S224, it is determined whether or not thecalculated predicted switching function value σpre(k) is in a rangedefined between a predetermined upper value XSGMH and a predeterminedlower limit value XSGML. If the calculated predicted switching functionvalue σpre(k) is in the range between XSGMH and XSGML, the process shownin FIG. 23 immediately ends. If the calculated predicted switchingfunction value σpre(k) is less than or equal to the predetermined lowerlimit value XSGML in step S223, the calculated predicted switchingfunction value σpre(k) is set to the predetermined lower limit valueXSGML in step S225. If the calculated predicted switching function valueσpre(k) is greater than or equal to the predetermined upper limit valueXSGMH in step S224, the calculated predicted switching function valueσpre(k) is set to the predetermined upper limit value XSGMH in stepS226.

[0187]FIG. 24 is a flowchart showing a process of calculating theswitching function setting parameter VPOLE, which is carried out in stepS221 shown in FIG. 23.

[0188] In step S231, it is determined whether or not the stabilitydetermination flag FSMCSTAB is “1”. If FSMCSTAB is “1” in step S231,indicating that the adaptive sliding mode controller 21 is unstable, theswitching function setting parameter VPOLE is set to a predeterminedstabilizing value XPOLESTB in step S232. The predetermined stabilizingvalue XPOLESTB is set to a value which is greater than “−1” but veryclose to “−1”(e.g., “−0.999”).

[0189] If FSMCSTAB is “0”, indicating that the adaptive sliding modecontroller 21 is stable, an amount of change DDTHR(k) in the targetvalue DTHR(k) is calculated from the following equation (46) in stepS233.

DDTHR(k)=DTHR(k)−DTHR(k−1)   (46)

[0190] In step S234, a VPOLE map is retrieved according to the throttlevalve opening deviation amount DTH and the amount of change DDTHRcalculated in step S233 to calculate the switching function settingparameter VPOLE. As shown in FIG. 25A, the VPOLE map is set so that theswitching function setting parameter VPOLE increases when the throttlevalve opening deviation amount DTH has a value in the vicinity of “0”,i.e., when the throttle valve opening TH is in the vicinity of thedefault opening THDEF, and the switching function setting parameterVPOLE has a substantially constant value regardless of changes of thethrottle valve opening deviation amount DTH, when the throttle valveopening deviation amount DTH has values which are not in the vicinity of“0”. The VPOLE map is also set so that the switching function settingparameter VPOLE increases as the amount of change DDTHR in target valueincreases as indicated by the solid line in FIG. 25B, and the switchingfunction setting parameter VPOLE increases as the amount of change DDTHRin the target value has a value in the vicinity of “0” as indicated bythe broken line in FIG. 25B, when the throttle valve opening deviationamount DTH has a value in the vicinity of “0”.

[0191] Specifically, when the target value DTHR for the throttle valveopening changes greatly in the decreasing direction, the switchingfunction setting parameter VPOLE is set to a relatively small value.This makes it possible to prevent the throttle valve 3 from collidingwith the stopper for stopping the throttle valve 3 in the fully closedposition. In the vicinity of the default opening THDEF, the switchingfunction setting parameter VPOLE is set to a relatively large value,which improves the controllability in the vicinity of the defaultopening THDEF.

[0192] As shown in FIG. 25C, the VPOLE map may be set so that theswitching function setting parameter VPOLE decreases when the throttlevalve opening TH is in the vicinity of the fully closed opening or thefully open opening. Therefore, when the throttle valve opening TH is inthe vicinity of the fully closed opening or the fully open opening, thespeed for the throttle valve opening TH to follow up the target openingTHR is reduced. As a result, collision of the throttle valve 3 with thestopper can more positively be avoided (the stopper also stops thethrottle valve 3 in the fully open position).

[0193] In steps S235 and S236, it is determined whether or not thecalculated switching function setting parameter VPOLE is in a rangedefined between a predetermined upper limit value XPOLEH and apredetermined lower limit XPOLEL. If the switching function settingparameter VPOLE is in the range between XPOLEH and XPOLEL, the processshown immediately ends. If the switching function setting parameterVPOLE is less than or equal to the predetermined lower limit valueXPOLEL in step S236, the switching function setting parameter VPOLE isset to the predetermined lower limit value XPOLEL in step S238. If theswitching function setting parameter VPOLE is greater than or equal tothe predetermined upper limit value XPOLEH in step S235, the switchingfunction setting parameter VPOLE is set to the predetermined upper limitvalue XPOLEH in step S237.

[0194]FIG. 26 is a flowchart showing a process of calculating anintegrated value of σpre, SUMSIGMA, of the predicted switching functionvalue σpre. This process is carried out in step S202 shown in FIG. 22.The integrated value SUMSIGMA is used for calculating the adaptive lawinput Uadp in the process shown in FIG. 28 which will be described later(see the equation (11a)).

[0195] In step S241, the integrated value SUMSIGMA is calculated fromthe following equation (47) where ΔT represents a calculation period.

SUMSIGMA(k)=SUMSIGMA(k−1)+σpre×ΔT   (47)

[0196] In steps S242 and S243, it is determined whether or not thecalculated integrated value SUMSIGMA is in a range defined between apredetermined upper limit value XSUMSH and a predetermined lower limitvalue XSUMSL. If the integrated value SUMSIGMA is in the range betweenXSUMSH and XSUMSL, the process immediately ends. If the integrated valueSUMSIGMA is less than or equal to the predetermined lower limit valueXSUMSL in step S242, the integrated value SUMSIGMA is set to thepredetermined lower limit value XSUMSL in step S244. If the integratedvalue SUMSIGMA is greater than or equal to the predetermined upper limitvalue XSUMSH in step S243, the integrated value SUMSIGMA is set to thepredetermined upper limit value XSUMSH in step S245.

[0197]FIG. 27 is a flowchart showing a process of calculating thereaching law input Urch, which is carried out in step S204 shown in FIG.22.

[0198] In step S261, it is determined whether or not the stabilitydetermination flag FSMCSTAB is “1”. If the stability determination flagFSMCSTAB is “0”, indicating that the adaptive sliding mode controller 21is stable, the control gain F is set to a normal gain XKRCH in stepS262, and the reaching law input Urch is calculated from the followingequation (48), which is the same as the equation (10a), in step S263.

Urch=−F×σpre/b 1   (48)

[0199] If the stability determination flag FSMCSTAB is “1”, indicatingthat the adaptive sliding mode controller 21 is unstable, the controlgain F is set to a predetermined stabilizing gain XKRCHSTB in step S264,and the reaching law input Urch is calculated according to the followingequation (49), which does not include the model parameter b1, in stepS265.

Urch=−F×σpre   (49)

[0200] In steps S266 and S267, it is determined whether the calculatedreaching law input Urch is in a range defined between a predeterminedupper limit value XURCHH and a predetermined lower limit value XURCHL.If the reaching law input Urch is in the range between XURCHH andXURCHL, the process immediately ends. If the reaching law input Urch isless than or equal to the predetermined lower limit value XURCHL in stepS266, the reaching law input Urch is set to the predetermined lowerlimit value XURCHL in step S268. If the reaching law input Urch isgreater than or equal to the predetermined upper limit value XURCHH instep S267, the reaching law input Urch is set to the predetermined upperlimit value XURCHH in step S269.

[0201] As described above, when the adaptive sliding mode controller 21becomes unstable, the control gain F is set to the predeterminedstabilizing gain XKRCHSTB, and the reaching law input Urch is calculatedwithout using the model parameter b1, which brings the adaptive slidingmode controller 21 back to its stable state. When the identifyingprocess carried out by the model parameter identifier 22 becomesunstable, the adaptive sliding mode controller 21 becomes unstable.Therefore, by using the equation (49) that does not include the modelparameter b1 which has become unstable, the adaptive sliding modecontroller 21 can be stabilized.

[0202]FIG. 28 is a flowchart showing a process of calculating theadaptive law input Uadp, which is carried out in step S205 shown in FIG.22.

[0203] In step S271, it is determined whether or not the stabilitydetermination flag FSMCSTAB is “1”. If the stability determination flagFSMCSTAB is “0”, indicating that the adaptive sliding mode controller 21is stable, the control gain G is set to a normal gain XKADP in stepS272, and the adaptive law input Uadp is calculated from the followingequation (50), which corresponds to the equation (11a), in step S273.

Uadp=−G×SUMSIGMA/b1   (50)

[0204] If the stability determination flag FSMCSTAB is “1”, indicatingthat the adaptive sliding mode controller 21 is unstable, the controlgain G is set to a predetermined stabilizing gain XKADPSTB in step S274,and the adaptive law input Uadp is calculated according to the followingequation (51), which does not include the model parameter b1, in stepS275.

Uadp=−G×SUMSIGMA   (51)

[0205] As described above, when the adaptive sliding mode controller 21becomes unstable, the control gain G is set to the predeterminedstabilizing gain XKADPSTB, and the adaptive law input Uadp is calculatedwithout using the model parameter b1, which brings the adaptive slidingmode controller 21 back to its stable state.

[0206]FIG. 29 is a flowchart showing a process of determining thestability of the sliding mode controller, which is carried out in stepS16 shown in FIG. 9. In this process, the stability is determined basedon a differential value of the Lyapunov function, and the stabilitydetermination flag FSMCSTAB is set according to the result of thestability determination.

[0207] In step S281, a switching function change amount Dopre iscalculated from the following equation (52). Then, a stabilitydetermining parameter SGMSTAB is calculated from the following equation(53) in step S282.

Dopre=σpre(k)−σpre(k−1)   (52)

SGMSTAB=Dopre×σpre(k)   (53)

[0208] In step S283, it is determined whether or not the stabilitydetermination parameter SGMSTAB is less than or equal to a stabilitydetermining threshold XSGMSTAB. If SGMSTAB is greater than XSGMSTAB, itis determined that the adaptive sliding mode controller 21 may possiblybe unstable, and an unstability detecting counter CNTSMCST isincremented by “1” in step S285. If SGMSTAB is less than or equal toXSGMSTAB, the adaptive sliding mode controller 21 is determined to bestable, and the count of the unstability detecting counter CNTSMCST isnot incremented but maintained in step S284.

[0209] In step S286, it is determined whether or not the value of theunstability detecting counter CNTSMCST is less than or equal to apredetermined count XSSTAB. If CNTSMCST is less than or equal to XSSTAB,the adaptive sliding mode controller 21 is determined to be stable, anda first determination flag FSMCSTAB1 is set to “0” in step S287. IfCNTSMCST is greater than XSSTAB, the adaptive sliding mode controller 21is determined to be unstable, and the first determination flag FSMCSTAB1is set to “1” in step S288. The value of the unstability detectingcounter CNTSMCST is initialized to “0”, when the ignition switch isturned on.

[0210] In step S289, a stability determining period counter CNTJUDST isdecremented by “1”. It is determined whether or not the value of thestability determining period counter CNTJUDST is “0” in step S290. Thevalue of the stability determining period counter CNTJUDST isinitialized to a predetermined determining count XCJUDST, when theignition switch is turned on. Initially, therefore, the answer to stepS290 is negative (NO), and the process immediately goes to step S295.

[0211] If the count of the stability determining period counter CNTJUDSTsubsequently becomes “0”, the process goes from step S290 to step S291,in which it is determined whether or not the first determination flagFSMCSTAB1 is “1”. If the first determination flag FSMCSTAB1 is “0”, asecond determination flag FSMCSTAB2 is set to “0” in step S293. If thefirst determination flag FSMCSTAB1 is “1”, the second determination flagFSMCSTAB2 is set to “1” in step S292.

[0212] In step S294, the value of the stability determining periodcounter CNTJUDST is set to the predetermined determining count XCJUDST,and the unstability detecting counter CNTSMCST is set to “0”.Thereafter, the process goes to step S295.

[0213] In step S295, the stability determination flag FSMCSTAB is set tothe logical sum of the first determination flag FSMCSTAB1 and the seconddetermination flag FSMCSTAB2. The second determination flag FSMCSTAB2 ismaintained at “1” until the value of the stability determining periodcounter CNTJUDST becomes “0”, even if the answer to step S286 becomesaffirmative (YES) and the first determination flag FSMCSTAB1 is set to“0”. Therefore, the stability determination flag FSMCSTAB is alsomaintained at “1” until the value of the stability determining periodcounter CNTJUDST becomes “0”.

[0214]FIG. 30 is a flowchart showing a process of calculating thedefault opening deviation thdefadp, which is carried out in step S17shown in FIG. 9.

[0215] In step S251 shown FIG. 30, a gain coefficient KPTH(k) iscalculated according to the following equation (54).

KPTH(k)=PTH(k−1)/(1+PTH(k−1))   (54)

[0216] where PTH(k−1) represents a gain parameter calculated in stepS253 when the present process was carried out in the preceding cycle.

[0217] In step S252, the model parameter c1′ calculated in the processof calculations of the model parameter identifier as shown in FIG. 11and the gain coefficient KPTH(k) calculated in step S251 are applied tothe following equation (55) to calculate a default opening deviationthdefadp(k).

thdefadp(k)=thdefadp(k−1) +KPTH(k)×(c1′−thdefadp(k−1))   (55)

[0218] In step S253, a gain parameter PTH(k) is calculated from thefollowing equation (56):

PTH(k)={1−PTH(k−1)}/(XDEFADPW+PTH(k−1))}×PTH(k−1)/XDEFADPW   (56)

[0219] The equation (56) is obtained by setting λ1′ and λ2′ in theequation (39) respectively to a predetermined value XDEFADP and “1”.

[0220] According to the process shown in FIG. 30, the model parameterc1′ is statistically processed by the sequentialmethod-of-weighted-least-squares algorithm to calculate the defaultopening deviation thdefadp.

[0221] In the present embodiment, the model parameter identifier 22shown in FIG. 3 corresponds to an identifying means, and the statepredictor 23 shown in FIG. 3 corresponds to a predicting means.

[0222] Although a certain preferred embodiment of the present inventionhas been shown and described in detail, it should be understood thatvarious changes and modifications may be made therein without departingfrom the scope of the appended claims.

What is claimed is:
 1. A control system for a throttle valve actuatingdevice including a throttle valve of an internal combustion engine andactuating means for actuating said throttle valve, said control systemincluding predicting means for predicting a future throttle valveopening and controlling said throttle actuating device according to thethrottle valve opening predicted by said predicting means so that thethrottle valve opening coincides with a target opening.
 2. A controlsystem according to claim 1, wherein said throttle valve actuatingdevice is modeled to a controlled object model which includes a deadtime element, and said predicting means predicts a throttle valveopening after the elapse of a dead time due to said dead time element,based on said controlled object model.
 3. A control system according toclaim 2, further including identifying means for identifying at leastone model parameter of said controlled object model, wherein saidpredicting means predicts the throttle valve opening using the at leastone model parameter identified by said identifying means.
 4. A controlsystem according to claim 1, further including a sliding mode controllerfor controlling said throttle valve actuating device with a sliding modecontrol according to a throttle valve opening predicted by saidpredicting means.
 5. A control system according to claim 2, furtherincluding a sliding mode controller for controlling said throttle valveactuating device with sliding mode control according to a throttle valveopening predicted by said predicting means, and identifying means foridentifying at least one model parameter of the controlled object model,wherein said sliding mode controller controls said throttle valveactuating device using the at least one model parameter identified bysaid identifying means.
 6. A control system according to claim 4,wherein the control input from said sliding mode controller to saidthrottle valve actuating device includes an adaptive law input.
 7. Acontrol method for controlling a throttle valve actuating deviceincluding a throttle valve of an internal combustion engine and anactuator for actuating said throttle valve, said control methodcomprising the steps of: a) predicting a future throttle valve opening;and b) controlling said throttle actuating device according to thepredicted throttle valve opening so that the throttle valve openingcoincides with a target opening.
 8. A control method according to claim7, wherein said throttle valve actuating device is modeled to acontrolled object model which includes a dead time element, and athrottle valve opening after the elapse of a dead time due to said deadtime element, is predicted based on said controlled object model.
 9. Acontrol method according to claim 8, further comprising the step ofidentifying at least one model parameter of said controlled objectmodel, wherein the throttle valve opening is predicted using theidentified at least one model parameter.
 10. A control method accordingto claim 7, wherein said throttle valve actuating device is controlledwith a sliding mode control according to the predicted throttle valveopening.
 11. A control method according to claim 8, further comprisingthe step of identifying at least one model parameter of the controlledobject model, wherein said throttle valve actuating device is controlledwith a sliding mode control according to the predicted throttle valveopening, using the identified at least one model parameter.
 12. Acontrol method according to claim 10, wherein the control input to saidthrottle valve actuating device includes an adaptive law input.
 13. Acontrol system for a throttle valve actuating device including athrottle valve of an internal combustion engine and an actuator foractuating said throttle valve, said control system including a predictorfor predicting a future throttle valve opening and controlling saidthrottle actuating device according to the throttle valve openingpredicted by said predictor so that the throttle valve opening coincideswith a target opening.
 14. A control system according to claim 13,wherein said throttle valve actuating device is modeled to a controlledobject model which includes a dead time element, and said predictorpredicts a throttle valve opening after the elapse of a dead time due tosaid dead time element, based on said controlled object model.
 15. Acontrol system according to claim 14, further including an identifierfor identifying at least one model parameter of said controlled objectmodel, wherein said predictor predicts the throttle valve opening usingthe at least one model parameter identified by said identifier.
 16. Acontrol system according to claim 13, further including a sliding modecontroller for controlling said throttle valve actuating device with asliding mode control according to a throttle valve opening predicted bysaid predictor.
 17. A control system according to claim 14, furtherincluding a sliding mode controller for controlling said throttle valveactuating device with a sliding mode control according to a throttlevalve opening predicted by said predictor, and an identifier foridentifying at least one model parameter of the controlled object model,wherein said sliding mode controller controls said throttle valveactuating device using the at least one model parameter identified bysaid identifier.
 18. A control system according to claim 16, wherein thecontrol input from said sliding mode controller to said throttle valveactuating device includes an adaptive law input.